OpenData Hub and OpenShift for AI/ML

1. HoJoong Kim
Sr. Manager, Red Hat
AI/ML with OpenShift

2. AI/ML 주요개념
Artificial Intelligence (AI)
Machines imitating intelligent human behavior. The
term AI is primarily used by the business community.
지능적인 인간 행동을 모방하는 기계
Machine Learning (ML)
Subset of AI, gives computers the ability to learn
without being explicitly programmed. The term ML is
primarily used by the technical community.
AI의 부분집합으로, 컴퓨터가 명시적으로
프로그래밍하지 않고도 학습할 수 있음
Deep Learning (DL)
Subset of ML, uses layers to progressively extract
higher level features from the raw input. Applications
include computer vision, image recognition, etc.
ML의 부분집합으로, 레이어를 사용하여 raw input에서 더
높은 레벨의 feature를 점진적으로 추출합니다.

3. Source: 2020 Machine Learning market estimate from Forbes
AI/ML Market

4. AI workflow

5. The ML lifecycle and responsibilities
Business
leadership
Data engineer
Data scientists
App developer
IT operations

6. The ML lifecycle

7. Two primary bottlenecks
● Getting started
● Getting into production

8. What is DevOps?
Where can I buy it?
CODE
BUILD TEST DEPLOY
MONITORREVIEW
Self-service
provisioning
Automated
build & deploy
CI/CD
pipelines
Consistent
environments
Configuration
management
App logs &
metrics

9. One platform for all the domains
CODE
BUILD TEST DEPLOY
MONITORREVIEW CODE
BUILD TEST DEPLOY
MONITORREVIEW CODE
BUILD TEST DEPLOY
MONITORREVIEW

10. The data scientist’s domain

11. Jupyter(Hub/Notebooks) in data science

12. Containers for models
Reproducible and shareable environments for building, training and serving
Python 2.x, 3.x
NumPy
Pandas
Matplotlib
Other Libraries
Container
Application
App Deps and Libs
Base Image
● Simpler, lighter, and denser than VMs
● Base image guarantees starting consistency
● Package apps with all dependencies
● Reusable, portable, shareable as a unit
Model

13. Open Data Hub

14. Open Data Hub: A Data & AI Platform for the Hybrid Cloud

15. Open Data Hub: High Level Architecture

16. Open Data Hub: Projects not products
● Unified analytics engine
● Large-scale data access
● Multi-user Jupyter
● Used for data science and
research
● Monitoring and alerting
toolkit
● Used to diagnose
problems
● Analytics platform for all
metrics
● Query, visualize and alert on
metrics
● Deploying machine learning
models as micro-services
● Full model lifecycle
management
● Distributed Object Store
● S3 Interface
● Distributed event streaming
● Pub/Sub Messaging
● Container-native workflow
engine
● Declaratively deploy ML
pipelines and models
* *

17. OpenShift

18. Project Echo
Project Tango
Jupyter-Notebook-as-a-Service
18
CPUs, GPUs, Memory,
NVMe
● Identity and RBAC
● Project isolation (data,
networks)
● Sharing of resources with
protection
● Resource isolation
● Resource quotas
● Priority and pre-emption
Data Scientist
Data Scientist
Jupyter Hub

19. 19
Simple AI/ML pipeline using OpenShift

20. 20
Accelerate AI with NVIDIA and Red Hat

21. 21
cnvrg.io and OpenShift MLOps solution

22. 22
Key use cases for machine learning on OpenShift

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